"Understanding Frontend and Backend Development" matching MCP servers:

MCP server for Next2D application development that generates code following MVVM + Clean Architecture + Atomic Design patterns, validates architecture, and provides API references.

Enables AI agents to analyze and scaffold React/Next.js applications enforcing Feature-Sliced Design (FSD) conventions, providing tools for project analysis, rule retrieval, and entity generation.

Enables AI to analyze, query, and manage a graph-based representation of software architecture for impact analysis, dependency tracking, and design.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

An MCP server that analyzes and traces business code flows across your codebase, enabling developers to quickly understand the complete execution path of features from frontend to backend to database.

Local-first cross-service code intelligence engine for AI agents, connecting frontend, gateways, backend services, and databases to enable impact analysis and change planning.

Cross-stack MCP server that scans frontend and backend code together, detects mismatches like phantom calls, dead endpoints, and field drift, and provides a full-stack health audit.

AI-native architecture diagramming for MCP clients (Claude Code, Cursor, Windsurf). The calling agent authors the graph (nodes, groups, edges); Flowgraf validates it, lays it out deterministically with ELK, renders an SVG + Mermaid, and returns a link to a live, editable canvas you can refine by chat, drag, or one-click AI design review. Agent-authored ops — no API key and no LLM cost to the calle

A comment-based spec-driven development MCP server that facilitates workflows from requirements creation to implementation via AI, using @spec-impl markers.

MCP server that provides a live, terminal-native layered dependency map for AI coding assistants, with tools to track and visualize development progress in real time.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Generates editable Excalidraw diagrams from natural language system descriptions, with properly bound arrows and labels. Includes tools for diagram creation, validation, and backend status.

An MCP server that recommends tech stacks for your project. Describe what you're building and get a recommended stack with reasoning, tradeoffs, and what each layer should use.

An MCP server designed for React Native and Expo development that provides specialized tools for project scaffolding, architectural best practices, and troubleshooting. It enables AI assistants to guide users through setup, navigation configuration, and CI/CD processes using modern stacks like NativeWind and Zustand.

A cross-project knowledge graph for MCP that maps features across mobile, backend, and admin codebases, giving AI agents full-stack context.

MCP server that creates, edits and exports architecture diagrams as .fluyo.json files, operating through tools like create_diagram, edit_diagram, and export_diagram directly from Claude.

A MCP server for SailfishOS application development, creates new projects and add features following guidelines, performs build

An MCP server for Spec-Driven Development that transforms natural language ideas and meeting transcripts into structured, production-grade specifications using EARS notation. It automates a 7-phase pipeline to generate project artifacts like requirements, architecture designs, and task lists directly to disk.

Enables AI assistants and other tools to create, edit, import, and export BPMN 2.0 workflow diagrams, including through a local browser-based live modeler with explicit save back to the session.

Bridges local development environments with the Engineering Intelligence Platform (EIP), enabling AI coding agents to manage SSOT, generate implementation/test/security plans, and synchronize architectural knowledge.

Provides programmatic access to Arcas OnlineEDA platform for electronic design automation, enabling formal verification, equivalence checking, power analysis, security verification, and FPGA design through natural language and automated workflows.

An MCP server implementing Spec-Driven Development workflows for AI-agent CLIs and IDEs like Claude Code and Cursor, enabling spec-first development with automated workflow guidance and quality checks.

Enables agents to analyze codebases (local or GitHub) and automatically generate Mermaid diagrams rendered as PNG images via Kroki, providing visual understanding of project structure and flow through file discovery, reading, and diagram generation.

MCP server for BESSER low-code platform enabling AI assistants to create, manipulate, and generate code from domain models through natural language. Supports model creation, deletion, and multi-language code generation (SQL, Python, Java, etc.) via the Model Context Protocol.

Enables interaction with IcePanel for architectural diagram management via MCP clients, such as Claude Desktop, Cursor, and Windsurf. Deprecated in favor of a remote MCP server.

Connects AI models to MasterGo design tools, enabling retrieval of DSL data, component documentation, and metadata from MasterGo design files for structured component development workflows.

Enables building, evolving, and managing microservices through natural language, with storage-agnostic REST APIs and support for relational, document, cloud, and embedded databases.

Enables AI agents to query architecture context, data contracts, and blast radius to prevent cross-repo architectural breakage before merging.

Builds a semantic graph of codebases across 38 languages and exposes 42 MCP tools for structured code understanding, search, and impact analysis.

Integrates Archaic's AI-powered architecture analysis with Claude Code, enabling interactive exploration of component connections, data flows, and system structure through natural language.